An improved pattern match method with flexible mask for automatic inspection in the LCD manufacturing process (original) (raw)
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International Journal of Optomechatronics, 2011
In this study an automatic optical inspection system is presented to evaluate the fracture and deformation status of conducting particles of anisotropic conductive film in the TFT-LCD assembly process. The amount of deformation and quantity of conducting particles in the test pattern can be automatically evaluated by image analysis. A specific operation is carried out in the image processing method, and the calculation of the image gradient operator 20 is used to produce a preferable contrast between the processed particle image and the background. The thinning processing method is applied for information reduction and information creation. An amount of samples are taken with a target template for synchronous multiple-comparison, and the optimal threshold of the binary image is obtained. This study utilizes the assistance of image processing technology to inspect the fracture conditions of 25 anisotropic conductive film in the TFT-LCD assembly process. This system can decrease the defection rate of products, obtain over 90% recognition accuracy even in noisy environments, and will be verified in an automatic production line.
It is well known that the quality of plastic electronics, printed circuit boards (PCB), and Organic Light Emitting Diode (OLED) displays can be variable in production. This in turn may lead to an increasing number of faulty batches. Digital radiography and pattern recognition allow inspection of the products non-destructively in real time to increase the quality of output batch for manufacturing lines. An important part of digital radiography for non-destructive testing (NDT) is image processing and pattern recognition. In this study, the whole chain of data processing is reconsidered starting from building representative reference data sets, image quality assessment, selecting region of interest, noise reduction, contrast enhancement, image segmentation. It is expected to consider different pattern recognition techniques such as active shape model, moving windows and greedy pursuit as well as traditional cross- correlation and distance template matching.
An Automatic Detection Algorithm for Surface Defects in TFT-LCD
The quality control of LCD manufacturing process is very important to minimize cost and maximize product quality. This paper proposes a practical algorithm that detects surface defects and identifies their types in thin film transistor liquid crystal display (TFT-LCD). The main contribution is to improve a local adaptive threshold method and apply it for extracting the defect candidates with a small brightness difference between the object and the background. Moreover, the defect types can be classified using blob shape analysis techniques. An automatic inspection system with the proposed algorithm has been developed and applied to real production line. Experimental results in-line show that it identifies defects in TFT-LCD images efficiently, and also achieves a high correctness in determining the types of defects.
Automated characterisation system for liquid crystal displays
2007 Spanish Conference on Electron Devices, 2007
A complete program suite for the automated characterisation of liquid crystal displays (LCDs) has been developed in the LabView 7.1 environment. It includes routines for basic electro-optical characterisation, i.e. generation of transmission-voltage curves applying triangular waveforms while measuring the switching voltages. And it provides an easily accessible interface for design of arbitrary waveforms, both for active matrix addressing (often necessary in nematic LCDs) and for passive matrix addressing (applicable to ferroand antiferroelectric LCDs as well as some nematic LCDs).
A review on Image Processing techniques using Pattern matching in LabVIEW
Image processing is one of the advanced technological methods used for various applications. There are several methods to implement image processing. Recent advancements and research study has proved that traditional algorithm based methods of image processing have not shown reliable accurate results. This paper attempts to bring out the possible approaches to implement image processing in LabVIEW, a user friendly graphical user interface software customized by National Instruments. Image processing in LabVIEW involves capturing the image of the object to be analyzed and comparing it with the reference image of the perfect one both geometrically and pattern wise. The essence of this technique in LabVIEW is that the accuracy and the percentage of matching could be set manually using the NI Vision Assistant Module.
IMAGE WAFER INSPECTION BASED ON TEMPLATE MATCHING
This paper presents a template matching technique for detecting defects in VLSI wafer images. This method is based on traditional techniques of image analysis and image registration, but it combines the prior art of image wafer inspection in a new way, using prior knowledge like the design layout of VLSI wafer manufacturing process. This technique requires a golden template of the patterned wafer image under inspection which is obtained from the wafer image itself mixed to the layout design schemes. First amapping between physical space and pixel space is needed. Then a template matching is applied for a more accurate alignment between wafer device and template. Finally, a segmented comparison is used for finding out possible defects. Results of the proposed method are presented in terms of visual quality of defect detection, any misalignment at topology level and number of correctly detected defective devices.
Automated Inspection System for Assembled Printed Circuit Board Using Machine Vision
Soft Computing Research Society eBooks, 2023
The perfect Printed Circuit Board (PCB) plays a very important role in every electronic device as well as in automation systems. So, it is very important to find defects in the PCB before installing it to any system or any device. However, PCB Manufacturers use various inspection systems in the process of manufacturing PCBs for detecting various types of defects in the PCB. In this article, we present the Automated assembled PCB Inspection System. This system finds defects such as missing components and improper position of its components by using the Pattern matching Technique where a good known score of template image is matched with the score of the test image. This system gives results at each inspection within 10 Seconds and the result given by this system are passed or fail in the form of an array sheet. This automated inspection system is created by using NI Vision Builder AI and NI LabVIEW technology. Ni Vision Builder AI has been used to create the algorithm. And NI LabVIEW has been used to create the application.
Automated Optical Inspection (AOI) Systems are commonly used on Printed Circuit Boards (PCB) manufacturing. The use of this technology has been proven as highly efficient for process improvements and quality achievements. The correct extraction of the component for posterior analysis is a critical step of the AOI process. Nowadays, the Pattern Matching Algorithm is commonly used, although this algorithm requires extensive calculations and is time-consuming​. This paper will present an improved algorithm for the component localization process, with the capability of implementation in a parallel execution system.
Quality inspection of engraved image using shape-based matching approach
2011
The role of machine vision system as a vital component for quality control mainly in manufacturing process cannot be denied. The system is developed to overcome the discrepancy from human vision and illumination changes. This paper proposes shape-based vision algorithm, a hierarchical templatematching approach that implemented in flexible manufacturing system to verify the quality of engraved image. Color and gray scale charged couple device (CCD) cameras are used to acquire engraved image for different kind of environment. The engraved image is preprocessed using image processing technique. Region of interest (ROI) is then selected and digitized into gray level to extract the contour of the object using segmentation technique. The extracted contour is used as template for object recognition during matching process. Several objects are engraved on the acrylic souvenir bases with different color background to test the algorithm. This experiment result shows that the algorithm works better with detection rate of 100% and matching accuracy of more than 98%. The approach can be applied in packaging, pharmacy, education, medical or any other areas which apply shape in their application.